Complex tools and processes may be prone to failure because of the numerous environmental and internal factors that affect performance. Failure includes both deviations (i.e., the tool or process operates using parameters outside their acceptable ranges) and shutdowns caused by, for example, mechanical failure or extreme deviations from acceptable process parameters. Often, there can be a substantial cost associated with the failure of complex tools or processes. Examples of failure related costs include operational downtime, equipment repair and servicing, and the like.
One approach to minimizing unexpected failure is to institute a program of periodic replacement, i.e., replacing tools or process systems when they are statistically expected to fail. While this approach may reduce unexpected failures, it cannot eliminate them, since periodic replacement is based on an average for all tools, not the characteristics and health of individual tools. For the same reason, overall operating costs may actually increase as good tools are needlessly replaced while unexpected failures continue to occur.
Traditionally, methods for predicting failures of complex tools and processes have focused on using time-series data collected for individual tools and using that data to predict the failure of each tool independently. Such traditional solutions are less than ideal because they produce individual models for each tool. Therefore, no generic model is available to predict failures across multiple tools, and a new model must be built for each new tool.
What is needed, therefore, is an approach by which an approaching tool failure is identified prior to its occurrence, i.e., advance failure prediction, using a generic model that can be applied to individual tools.
Cryogenic pump installations exemplify systems that employ multiple tools having similar failure profiles. During normal operation in such installations, cryogenic pumps measure and regulate temperature. Each pump may sense or experience dozens of conditions affecting the operation of the pump. In addition, numerous pumps are employed at any given installation. As a result, advance failure prediction of an individual pump at any given time is difficult, yet unexpected failure of a pump can cause disruption and increase operating costs.